code: def train(
      model,
      train_dataset,
      val_dataset=None,
      optimizer=None,
      save_dir="output",
      iters=10000,
      batch_size=2,
      resume_model=None,
      save_interval=1000,
      log_iters=10,
      num_workers=0,
      use_vdl=False,
      losses=None,
      keep_checkpoint_max=5,
      precision="fp32",
      profiler_options=None,
      to_static_training=False,
  ):
      """
      Launch training.
  
      Args:
          model（nn.Layer): A sementic segmentation model.
          train_dataset (paddle.io.Dataset): Used to read and process training datasets.
          val_dataset (paddle.io.Dataset, optional): Used to read and process validation datasets.
          optimizer (paddle.optimizer.Optimizer): The optimizer.
          save_dir (str, optional): The directory for saving the model snapshot. Default: 'output'.
          iters (int, optional): How may iters to train the model. Defualt: 10000.
          batch_size (int, optional): Mini batch size of one gpu or cpu. Default: 2.
          resume_model (str, optional): The path of resume model.
          save_interval (int, optional): How many iters to save a model snapshot once during training. Default: 1000.
          log_iters (int, optional): Display logging information at every log_iters. Default: 10.
          num_workers (int, optional): Num workers for data loader. Default: 0.
          use_vdl (bool, optional): Whether to record the data to VisualDL during training. Default: False.
          losses (dict, optional): A dict including 'types' and 'coef'. The length of coef should equal to 1 or len(losses['types']).
              The 'types' item is a list of object of paddleseg.models.losses while the 'coef' item is a list of the relevant coefficient.
          keep_checkpoint_max (int, optional): Maximum number of checkpoints to save. Default: 5.
          profiler_options (str, optional): The option of train profiler.
          to_static_training (bool, optional): Whether to use @to_static for training.
      """
      model.train()
      start_iter = 0
      if resume_model is not None:
          start_iter = resume(model, optimizer, resume_model)
  
      if not os.path.isdir(save_dir):
          if os.path.exists(save_dir):
              os.remove(save_dir)
          os.makedirs(save_dir)
  
      loader = train_dataset
  
      if use_vdl:
          from visualdl import LogWriter
  
          log_writer = LogWriter(save_dir)
  
      if to_static_training:
          model = paddle.jit.to_static(model)
          logger.info("Successfully to apply @to_static")
  
      avg_loss = 0.0
      avg_loss_list = []
      mdice = 0.0
      channel_dice_array = np.array([])
      iters_per_epoch = len(loader)
  
      best_mean_dice = -1.0
      best_model_iter = -1
      reader_cost_averager = TimeAverager()
      batch_cost_averager = TimeAverager()
      save_models = deque()
      batch_start = time.time()
  
      if precision == "fp16":
          scaler = paddle.amp.GradScaler()
  
      iter = start_iter
      while iter < iters:
          for data in loader:
              reader_cost_averager.record(time.time() - batch_start)
              images = data[0]
              labels = data[1]
  
              if precision == "fp16":
                  with paddle.amp.auto_cast():
                      logits_list = model(images)
                      loss_list, per_channel_dice = loss_computation(
                          logits_list=logits_list,
                          labels=labels,
                          losses=losses,
                      )
                      loss = sum(loss_list)
                  scaler.scale(loss).backward()
                  scaler.unscale_(optimizer)
                  scaler.step(optimizer)
                  scaler.update()
              else:
                  logits_list = model(images)
  
                  loss_list, per_channel_dice = loss_computation(
                      logits_list=logits_list,
                      labels=labels,
                      losses=losses,
                  )
                  loss = sum(loss_list)
                  loss.backward()
                  optimizer.step()
  
              lr = optimizer.get_lr()
              iter += 1
              if iter > iters:  # 防止msd训练停不下来
                  break
  
              # update lr
              lr_sche = optimizer._learning_rate
              if isinstance(lr_sche, paddle.optimizer.lr.LRScheduler):
                  lr_sche.step()
  
              train_profiler.add_profiler_step(profiler_options)
  
              model.clear_gradients()
              avg_loss += float(loss)
              mdice += np.mean(per_channel_dice) * 100
  
              if channel_dice_array.size == 0:
                  channel_dice_array = per_channel_dice
              else:
                  channel_dice_array += per_channel_dice
  
              if len(avg_loss_list) == 0:
                  avg_loss_list = [l.numpy() for l in loss_list]
              else:
                  for i in range(len(loss_list)):
                      avg_loss_list[i] += loss_list[i].numpy()
  
              batch_cost_averager.record(
                  time.time() - batch_start, num_samples=batch_size
              )
  
              if (iter) % log_iters == 0:
                  avg_loss /= log_iters
                  avg_loss_list = [
                      l.item() / log_iters for l in avg_loss_list
                  ]
                  mdice /= log_iters
                  channel_dice_array = channel_dice_array / log_iters
  
                  remain_iters = iters - iter
                  avg_train_batch_cost = (
                      batch_cost_averager.get_average()
                  )
                  avg_train_reader_cost = (
                      reader_cost_averager.get_average()
                  )
                  eta = calculate_eta(
                      remain_iters, avg_train_batch_cost
                  )
                  logger.info(
                      "[TRAIN] epoch: {}, iter: {}/{}, loss: {:.4f}, DSC: {:.4f}, "
                      "lr: {:.6f}, batch_cost: {:.4f}, reader_cost: {:.5f}, ips: {:.4f} samples/sec | ETA {}".format(
                          (iter) // iters_per_epoch,
                          iter,
                          iters,
                          avg_loss,
                          mdice,
                          lr,
                          avg_train_batch_cost,
                          avg_train_reader_cost,
                          batch_cost_averager.get_ips_average(),
                          eta,
                      )
                  )
  
                  if use_vdl:
                      log_writer.add_scalar(
                          "Train/loss", avg_loss, iter
                      )
                      # Record all losses if there are more than 2 losses.
                      if len(avg_loss_list) > 1:
                          for i, loss in enumerate(avg_loss_list):
                              log_writer.add_scalar(
                                  "Train/loss_{}".format(i), loss, iter
                              )
  
                      log_writer.add_scalar("Train/mdice", mdice, iter)
                      log_writer.add_scalar("Train/lr", lr, iter)
                      log_writer.add_scalar(
                          "Train/batch_cost", avg_train_batch_cost, iter
                      )
                      log_writer.add_scalar(
                          "Train/reader_cost",
                          avg_train_reader_cost,
                          iter,
                      )
                  avg_loss = 0.0
                  avg_loss_list = []
                  mdice = 0.0
                  channel_dice_array = np.array([])
                  reader_cost_averager.reset()
                  batch_cost_averager.reset()
  
              if (iter % save_interval == 0 or iter == iters) and (
                  val_dataset is not None
              ):
                  num_workers = 1 if num_workers > 0 else 0
  
                  result_dict = evaluate(
                      model,
                      val_dataset,
                      losses,
                      num_workers=num_workers,
                      writer=log_writer,
                      print_detail=True,
                      auc_roc=False,
                      save_dir=save_dir,
                  )
  
                  model.train()
  
              if iter % save_interval == 0 or iter == iters:
                  current_save_dir = os.path.join(
                      save_dir, "iter_{}".format(iter)
                  )
                  os.makedirs(current_save_dir, exist_ok=True)
                  paddle.save(
                      model.state_dict(),
                      os.path.join(current_save_dir, "model.pdparams"),
                  )
                  paddle.save(
                      optimizer.state_dict(),
                      os.path.join(current_save_dir, "model.pdopt"),
                  )
                  save_models.append(current_save_dir)
                  if len(save_models) > keep_checkpoint_max > 0:
                      model_to_remove = save_models.popleft()
                      shutil.rmtree(model_to_remove)
  
                  if val_dataset is not None:
                      if result_dict["mdice"] > best_mean_dice:
                          best_mean_dice = result_dict["mdice"]
                          best_model_iter = iter
                          best_model_dir = os.path.join(
                              save_dir, "best_model"
                          )
                          paddle.save(
                              model.state_dict(),
                              os.path.join(
                                  best_model_dir, "model.pdparams"
                              ),
                          )
                      logger.info(
                          "[EVAL] The model with the best validation mDice ({:.4f}) was saved at iter {}.".format(
                              best_mean_dice, best_model_iter
                          )
                      )
  
                      if use_vdl:
                          log_writer.add_scalar(
                              "Evaluate/Dice",
                              result_dict["mdice"],
                              iter,
                          )
                          if "auc_roc" in result_dict:
                              log_writer.add_scalar(
                                  "Evaluate/auc_roc",
                                  result_dict["auc_roc"],
                                  iter,
                              )
  
              batch_start = time.time()
  
      _ = paddle.flops(
          model,
          [1, *images.shape[1:]],
          custom_ops={
              paddle.nn.SyncBatchNorm: op_flops_run.count_syncbn
          },
      )
  
      # Sleep for half a second to let dataloader release resources.
      time.sleep(0.5)
      if use_vdl:
          log_writer.close()
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/core/train.py
imports: import os
  import time
  import shutil
  import numpy
  from collections import deque
  import paddle
  import paddle.nn.functional
  from medicalseg.utils import TimeAverager, calculate_eta, resume, logger, worker_init_fn, train_profiler, op_flops_run, loss_computation
  from nnunet.core.val import evaluate
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, train_dataset, val_dataset, optimizer, save_dir, iters, batch_size, resume_model, save_interval, log_iters, num_workers, use_vdl, losses, keep_checkpoint_max, precision, profiler_options, to_static_training) without return types